This is a data set that includes the following variables:
| DH1_W_Responsible |
| DH2_W_Civilized |
| DH3_W_Moral |
| DH4_W_Polite |
| DH5_W_Childlike |
| DH6_W_Rational |
| DH7_W_Warm |
| DH8_W_Agentic |
| DH9_W_Refined |
| DH10_W_Lacking_Culture |
| DH11_W_Lacking_Self-restraint |
| DH12_W_Instinctual |
| DH13_W_Mature |
| DH14_W_Stoic |
| DH15_W_Emotionally_Responsive |
| DH16_W_Cold |
| DH17_W_Open |
| DH18_W_Rigid |
| DH19_W_Passive |
| DH20_W_Superficial |
| DH1_N_Responsible |
| DH2_N_Civilized |
| DH3_N_Moral |
| DH4_N_Polite |
| DH5_N_Childlike |
| DH6_N_Rational |
| DH7_N_Warm |
| DH8_N_Agentic |
| DH9_N_Refined |
| DH10_N_Lacking_Culture |
| DH11_N_Lacking_Self-restraint |
| DH12_N_Instinctual |
| DH13_N_Mature |
| DH14_N_Stoic |
| DH15_N_Emotionally_Responsive |
| DH16_N_Cold |
| DH17_N_Open |
| DH18_N_Rigid |
| DH19_N_Passive |
| DH20_N_Superficial |
| No_Columbus |
| Create_IPD |
| BorninUS |
| LengthState |
| FatherUSBorn |
| MotherUSBorn |
| PrimaryCaretaker |
| PrimaryEd |
| SecondCaretaker |
| SecondEdu |
| Religion |
| Religiosity |
| College |
| Year |
| Education |
| LibCon |
| SES |
| Race |
| StateNumeric |
| LongestRegion |
| SubjectNumber |
| EMP_comp |
| EMP_cent |
| White_Dehumanize |
| Native_Dehumanize |
| Age |
It’s primary focus is to look a bit more closely at the dehumaniztion variables and how they interact not only with one another, but with the dependent variables (Columbus Day/Indigenous Peoples’ Day (henceforth referred to as IPD)), as well as some of the demographic information gathered on the participants in this sample.
This sample comes from Study Two, so it is (if I’m not mistaken) a sample of participants gathered via mturk.
First, lets take a look at a descriptives table of our variables. Keep in mind that some of the variables are categorical and are more difficult to interpret in a descriptives table.
| n | mean | sd | median | se | |
|---|---|---|---|---|---|
| DH1_W_Responsible | 2811 | 61.24 | 21.82 | 62 | 0.4116 |
| DH2_W_Civilized | 2810 | 65.89 | 23.38 | 69 | 0.4411 |
| DH3_W_Moral | 2812 | 55.77 | 22.76 | 55 | 0.4292 |
| DH4_W_Polite | 2812 | 57.99 | 22.53 | 58 | 0.4248 |
| DH5_W_Childlike | 2809 | 40.76 | 24.49 | 43 | 0.462 |
| DH6_W_Rational | 2810 | 58.23 | 22.49 | 57 | 0.4243 |
| DH7_W_Warm | 2810 | 58.27 | 22.32 | 58 | 0.421 |
| DH8_W_Agentic | 2800 | 46.11 | 22.34 | 50 | 0.4222 |
| DH9_W_Refined | 2809 | 52.87 | 22.26 | 52 | 0.4199 |
| DH10_W_Lacking_Culture | 2808 | 46.34 | 27.71 | 50 | 0.523 |
| DH11_W_Lacking_Self-restraint | 2808 | 48.59 | 25.58 | 50 | 0.4828 |
| DH12_W_Instinctual | 2810 | 51.47 | 22.98 | 51 | 0.4335 |
| DH13_W_Mature | 2809 | 57.34 | 22.31 | 56 | 0.421 |
| DH14_W_Stoic | 2808 | 42.88 | 22.31 | 49 | 0.4209 |
| DH15_W_Emotionally_Responsive | 2812 | 59.98 | 22.77 | 60 | 0.4294 |
| DH16_W_Cold | 2810 | 44.74 | 23.99 | 49 | 0.4525 |
| DH17_W_Open | 2811 | 55.59 | 22.81 | 55 | 0.4302 |
| DH18_W_Rigid | 2807 | 49.6 | 23.27 | 50 | 0.4393 |
| DH19_W_Passive | 2810 | 45.16 | 23.21 | 49 | 0.4378 |
| DH20_W_Superficial | 2810 | 59.32 | 25.54 | 60 | 0.4818 |
| DH1_N_Responsible | 2807 | 63.86 | 21.06 | 64 | 0.3976 |
| DH2_N_Civilized | 2806 | 64.58 | 21.97 | 65 | 0.4147 |
| DH3_N_Moral | 2807 | 65.75 | 20.88 | 66 | 0.3941 |
| DH4_N_Polite | 2806 | 63.22 | 21.42 | 63 | 0.4044 |
| DH5_N_Childlike | 2802 | 29.22 | 22.26 | 25 | 0.4205 |
| DH6_N_Rational | 2807 | 61.61 | 21.17 | 61 | 0.3995 |
| DH7_N_Warm | 2804 | 61.05 | 22.07 | 60 | 0.4167 |
| DH8_N_Agentic | 2800 | 44.97 | 22.32 | 50 | 0.4219 |
| DH9_N_Refined | 2806 | 51.11 | 22.44 | 51 | 0.4235 |
| DH10_N_Lacking_Culture | 2804 | 23.9 | 23.15 | 17 | 0.4371 |
| DH11_N_Lacking_Self-restraint | 2805 | 34.44 | 23.39 | 33 | 0.4416 |
| DH12_N_Instinctual | 2807 | 61.3 | 23.48 | 61 | 0.4433 |
| DH13_N_Mature | 2806 | 65.15 | 20.69 | 65 | 0.3907 |
| DH14_N_Stoic | 2808 | 59.11 | 22.29 | 57 | 0.4206 |
| DH15_N_Emotionally_Responsive | 2806 | 54.38 | 24.29 | 53 | 0.4585 |
| DH16_N_Cold | 2805 | 35.19 | 22.8 | 35 | 0.4306 |
| DH17_N_Open | 2805 | 53.8 | 23.34 | 53 | 0.4406 |
| DH18_N_Rigid | 2807 | 45.41 | 23.89 | 50 | 0.451 |
| DH19_N_Passive | 2805 | 45.03 | 23.79 | 50 | 0.4492 |
| DH20_N_Superficial | 2805 | 30.41 | 22.87 | 27 | 0.4318 |
| No_Columbus | 2858 | 3.793 | 2.125 | 4 | 0.03976 |
| Create_IPD | 2858 | 4.481 | 1.956 | 4 | 0.03658 |
| BorninUS* | 2801 | 1.956 | 0.2041 | 2 | 0.003857 |
| LengthState | 2797 | 30.72 | 12.88 | 29 | 0.2435 |
| FatherUSBorn* | 2801 | 1.893 | 0.3352 | 2 | 0.006334 |
| MotherUSBorn* | 2801 | 1.882 | 0.3337 | 2 | 0.006306 |
| PrimaryCaretaker* | 2800 | 1.282 | 0.7601 | 1 | 0.01436 |
| PrimaryEd | 2801 | 2.988 | 1.225 | 3 | 0.02315 |
| SecondCaretaker* | 2801 | 2.828 | 1.114 | 3 | 0.02105 |
| SecondEdu | 2736 | 2.908 | 1.323 | 2 | 0.02529 |
| Religion* | 2799 | 2.45 | 1.674 | 2 | 0.03164 |
| Religiosity | 2800 | 3.632 | 2.235 | 4 | 0.04224 |
| College* | 2799 | 1.893 | 0.3094 | 2 | 0.005848 |
| Year | 300 | 3.363 | 1.631 | 3 | 0.09417 |
| Education | 2800 | 3.459 | 1.127 | 4 | 0.0213 |
| LibCon | 2799 | 4.401 | 1.713 | 4 | 0.03237 |
| SES* | 2801 | 1.538 | 0.4986 | 2 | 0.009422 |
| Race* | 2903 | 3.42 | 1.684 | 3 | 0.03125 |
| StateNumeric* | 2788 | 24.73 | 14.36 | 25 | 0.272 |
| LongestRegion* | 2789 | 4.425 | 2.104 | 5 | 0.03984 |
| SubjectNumber | 2903 | 1452 | 838.2 | 1452 | 15.56 |
| EMP_comp | 2841 | 3.337 | 1.438 | 3.4 | 0.02698 |
| EMP_cent | 2841 | 1.063e-16 | 1.438 | 0.06341 | 0.02698 |
| White_Dehumanize | 2793 | 52.93 | 12.03 | 53.25 | 0.2275 |
| Native_Dehumanize | 2792 | 50.67 | 11.38 | 51.05 | 0.2153 |
| Age | 2793 | 38.72 | 13.33 | 35 | 0.2523 |
##
## Pearson's product-moment correlation
##
## data: dfdehumanDV$DH2_N_Civilized and dfdehumanDV$DH5_N_Childlike
## t = -11.141, df = 2800, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2412194 -0.1702999
## sample estimates:
## cor
## -0.2060302
At baseline the two seem to be somewhat negatively correlated with scores clustering around the Natives as Civilized and childlike.
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for getting rid of Columbus Day?
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
Here is the Childlike model uncentered:
##
## Call:
## lm(formula = No_Columbus ~ DH5_N_Childlike + DH5_W_Childlike +
## DH5_N_Childlike * DH5_W_Childlike, data = dv4cor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0529 -1.8904 0.0164 1.9077 4.0183
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.282e+00 1.014e-01 32.350 < 2e-16 ***
## DH5_N_Childlike -5.454e-03 3.656e-03 -1.492 0.136
## DH5_W_Childlike 1.771e-02 2.295e-03 7.719 1.62e-14 ***
## DH5_N_Childlike:DH5_W_Childlike -4.217e-05 6.721e-05 -0.627 0.530
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.093 on 2798 degrees of freedom
## (101 observations deleted due to missingness)
## Multiple R-squared: 0.03255, Adjusted R-squared: 0.03151
## F-statistic: 31.38 on 3 and 2798 DF, p-value: < 2.2e-16
…and here it is centered:
##
## Call:
## lm(formula = No_Columbus ~ scale(DH5_N_Childlike, scale = F) +
## scale(DH5_W_Childlike, scale = F) + scale(DH5_N_Childlike,
## scale = F) * scale(DH5_W_Childlike, scale = F), data = dv4cor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0529 -1.8904 0.0164 1.9077 4.0183
##
## Coefficients:
## Estimate
## (Intercept) 3.794e+00
## scale(DH5_N_Childlike, scale = F) -7.172e-03
## scale(DH5_W_Childlike, scale = F) 1.648e-02
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) -4.217e-05
## Std. Error
## (Intercept) 4.171e-02
## scale(DH5_N_Childlike, scale = F) 1.944e-03
## scale(DH5_W_Childlike, scale = F) 1.793e-03
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 6.721e-05
## t value
## (Intercept) 90.962
## scale(DH5_N_Childlike, scale = F) -3.689
## scale(DH5_W_Childlike, scale = F) 9.189
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) -0.627
## Pr(>|t|)
## (Intercept) < 2e-16
## scale(DH5_N_Childlike, scale = F) 0.000229
## scale(DH5_W_Childlike, scale = F) < 2e-16
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 0.530454
##
## (Intercept) ***
## scale(DH5_N_Childlike, scale = F) ***
## scale(DH5_W_Childlike, scale = F) ***
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.093 on 2798 degrees of freedom
## (101 observations deleted due to missingness)
## Multiple R-squared: 0.03255, Adjusted R-squared: 0.03151
## F-statistic: 31.38 on 3 and 2798 DF, p-value: < 2.2e-16
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for getting rid of Columbus Day?
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
Here is the Civilized model uncentered:
##
## Call:
## lm(formula = No_Columbus ~ DH2_N_Civilized, data = dv4cor2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9395 -1.8397 0.1559 2.1429 3.4944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.505559 0.124571 28.141 <2e-16 ***
## DH2_N_Civilized 0.004340 0.001826 2.376 0.0176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.125 on 2804 degrees of freedom
## (97 observations deleted due to missingness)
## Multiple R-squared: 0.00201, Adjusted R-squared: 0.001654
## F-statistic: 5.647 on 1 and 2804 DF, p-value: 0.01755
…and here it is centered:
##
## Call:
## lm(formula = No_Columbus ~ scale(DH2_N_Civilized, scale = F),
## data = dv4cor2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9395 -1.8397 0.1559 2.1429 3.4944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.785816 0.040111 94.384 <2e-16 ***
## scale(DH2_N_Civilized, scale = F) 0.004340 0.001826 2.376 0.0176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.125 on 2804 degrees of freedom
## (97 observations deleted due to missingness)
## Multiple R-squared: 0.00201, Adjusted R-squared: 0.001654
## F-statistic: 5.647 on 1 and 2804 DF, p-value: 0.01755
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for establishing an Indigenous Peoples’ Day?
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
Here is the Childlike model uncentered:
##
## Call:
## lm(formula = Create_IPD ~ DH5_N_Childlike + DH5_W_Childlike +
## DH5_N_Childlike * DH5_W_Childlike, data = dv5cor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4267 -1.2536 -0.1019 1.6597 3.3779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.253e+00 9.372e-02 45.382 < 2e-16 ***
## DH5_N_Childlike -1.148e-02 3.378e-03 -3.397 0.000691 ***
## DH5_W_Childlike 1.173e-02 2.120e-03 5.535 3.41e-08 ***
## DH5_N_Childlike:DH5_W_Childlike 5.913e-05 6.209e-05 0.952 0.341053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.933 on 2798 degrees of freedom
## (101 observations deleted due to missingness)
## Multiple R-squared: 0.02507, Adjusted R-squared: 0.02403
## F-statistic: 23.99 on 3 and 2798 DF, p-value: 2.523e-15
…and here it is centered:
##
## Call:
## lm(formula = Create_IPD ~ scale(DH5_N_Childlike, scale = F) +
## scale(DH5_W_Childlike, scale = F) + scale(DH5_N_Childlike,
## scale = F) * scale(DH5_W_Childlike, scale = F), data = dv5cor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4267 -1.2536 -0.1019 1.6597 3.3779
##
## Coefficients:
## Estimate
## (Intercept) 4.467e+00
## scale(DH5_N_Childlike, scale = F) -9.066e-03
## scale(DH5_W_Childlike, scale = F) 1.346e-02
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 5.913e-05
## Std. Error
## (Intercept) 3.854e-02
## scale(DH5_N_Childlike, scale = F) 1.796e-03
## scale(DH5_W_Childlike, scale = F) 1.657e-03
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 6.209e-05
## t value
## (Intercept) 115.909
## scale(DH5_N_Childlike, scale = F) -5.047
## scale(DH5_W_Childlike, scale = F) 8.125
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 0.952
## Pr(>|t|)
## (Intercept) < 2e-16
## scale(DH5_N_Childlike, scale = F) 4.77e-07
## scale(DH5_W_Childlike, scale = F) 6.65e-16
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F) 0.341
##
## (Intercept) ***
## scale(DH5_N_Childlike, scale = F) ***
## scale(DH5_W_Childlike, scale = F) ***
## scale(DH5_N_Childlike, scale = F):scale(DH5_W_Childlike, scale = F)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.933 on 2798 degrees of freedom
## (101 observations deleted due to missingness)
## Multiple R-squared: 0.02507, Adjusted R-squared: 0.02403
## F-statistic: 23.99 on 3 and 2798 DF, p-value: 2.523e-15
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for establishing an Indegenous Peoples’ Day?
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
Here is the Civilized model uncentered:
##
## Call:
## lm(formula = Create_IPD ~ DH2_N_Civilized, data = dv5cor2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8506 -1.2217 0.1033 1.6211 3.1976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.80235 0.11392 33.376 <2e-16 ***
## DH2_N_Civilized 0.01048 0.00167 6.277 4e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.943 on 2804 degrees of freedom
## (97 observations deleted due to missingness)
## Multiple R-squared: 0.01386, Adjusted R-squared: 0.0135
## F-statistic: 39.4 on 1 and 2804 DF, p-value: 3.997e-10
…and here it is centered:
##
## Call:
## lm(formula = Create_IPD ~ scale(DH2_N_Civilized, scale = F),
## data = dv5cor2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8506 -1.2217 0.1033 1.6211 3.1976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.47933 0.03668 122.109 <2e-16 ***
## scale(DH2_N_Civilized, scale = F) 0.01048 0.00167 6.277 4e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.943 on 2804 degrees of freedom
## (97 observations deleted due to missingness)
## Multiple R-squared: 0.01386, Adjusted R-squared: 0.0135
## F-statistic: 39.4 on 1 and 2804 DF, p-value: 3.997e-10
We can also look at both of these groups side by side:
and…
For Civilized
##
## Call:
## lm(formula = Rating ~ DH2_N_Civilized + Holiday_Support + DH2_N_Civilized:Holiday_Support,
## data = dv4and5cor2.lng.cen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8506 -1.7659 0.1494 1.6735 3.4944
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.479330 0.038435 116.542
## DH2_N_Civilized 0.010483 0.001750 5.990
## Holiday_SupportNo_Columbus -0.693514 0.054356 -12.759
## DH2_N_Civilized:Holiday_SupportNo_Columbus -0.006143 0.002475 -2.482
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## DH2_N_Civilized 2.22e-09 ***
## Holiday_SupportNo_Columbus < 2e-16 ***
## DH2_N_Civilized:Holiday_SupportNo_Columbus 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.036 on 5608 degrees of freedom
## (194 observations deleted due to missingness)
## Multiple R-squared: 0.03524, Adjusted R-squared: 0.03472
## F-statistic: 68.27 on 3 and 5608 DF, p-value: < 2.2e-16
For Childlike
##
## Call:
## lm(formula = Rating ~ DH5_N_Childlike + Holiday_Support + DH5_N_Childlike:Holiday_Support,
## data = dv4and5cor.lng.cen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5808 -1.7851 0.2009 1.6018 3.2646
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.478230 0.038595 116.031
## DH5_N_Childlike -0.003512 0.001734 -2.025
## Holiday_SupportNo_Columbus -0.692719 0.054582 -12.691
## DH5_N_Childlike:Holiday_SupportNo_Columbus 0.002804 0.002453 1.143
## Pr(>|t|)
## (Intercept) <2e-16 ***
## DH5_N_Childlike 0.0429 *
## Holiday_SupportNo_Columbus <2e-16 ***
## DH5_N_Childlike:Holiday_SupportNo_Columbus 0.2530
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.043 on 5600 degrees of freedom
## (202 observations deleted due to missingness)
## Multiple R-squared: 0.02868, Adjusted R-squared: 0.02816
## F-statistic: 55.11 on 3 and 5600 DF, p-value: < 2.2e-16
Let’s dig into some of the demographic information in this subset of the data.
First let’s look at the frequency of our different racial groups
| Asian | Black | White | Latino | Middle Eastern | Native | Other | Multiracial |
|---|---|---|---|---|---|---|---|
| 139 | 224 | 2097 | 106 | 4 | 17 | 24 | 292 |
Since our data skews toward Asian, Black, White, and Latino let’s use these groups in future analysis.
First off, let’s look at how our different racial groups answered the question about Columbus Day.
Next let’s see how these groups answered the question about Indigenous Peoples’ Day
For the next set of graphs let’s revisit the question of dehumanization. What we’re interested in here is whether or not responses to the questions of dehumanization differed by racial group.
| Race | N | DH2_N_Civilized | sd | se | ci |
|---|---|---|---|---|---|
| Asian | 139 | 57.88 | 21.02 | 1.783 | 3.526 |
| Black | 223 | 67.96 | 25.11 | 1.681 | 3.313 |
| White | 2093 | 64.3 | 21.5 | 0.4699 | 0.9216 |
| Latino | 106 | 68.56 | 19.53 | 1.897 | 3.762 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 3 10579 3526 7.465 5.65e-05 ***
## Residuals 2557 1207899 472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 5 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DH2_N_Civilized ~ Race, data = dfdehumanDV.race)
##
## $Race
## diff lwr upr p adj
## Black-Asian 10.0792335 4.041146 16.1173214 0.0001084
## White-Asian 6.4165890 1.522633 11.3105450 0.0042355
## Latino-Asian 10.6717117 3.466816 17.8766072 0.0008266
## White-Black -3.6626444 -7.598482 0.2731935 0.0787866
## Latino-Black 0.5924782 -5.999224 7.1841801 0.9956555
## Latino-White 4.2551226 -1.307506 9.8177510 0.2010351
This analysis is done via One way ANOVA using ratings of how civilized Natives are as the DV
Now let’s look at those same tables and bar graphs for ratings of how childlike Natives are:
| Race | N | DH5_N_Childlike | sd | se | ci |
|---|---|---|---|---|---|
| Asian | 139 | 35.21 | 22.16 | 1.879 | 3.716 |
| Black | 222 | 23.43 | 23.63 | 1.586 | 3.125 |
| White | 2091 | 29.22 | 21.65 | 0.4735 | 0.9286 |
| Latino | 106 | 32.21 | 26.81 | 2.604 | 5.163 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 3 13372 4457 9.134 5.19e-06 ***
## Residuals 2554 1246388 488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 8 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DH5_N_Childlike ~ Race, data = dfdehumanDV.race)
##
## $Race
## diff lwr upr p adj
## Black-Asian -11.780705 -17.923150 -5.638260 0.0000052
## White-Asian -5.984817 -10.959204 -1.010429 0.0107867
## Latino-Asian -3.001086 -10.324175 4.322003 0.7178329
## White-Black 5.795888 1.787167 9.804610 0.0011799
## Latino-Black 8.779619 2.074923 15.484315 0.0042992
## Latino-White 2.983731 -2.670280 8.637742 0.5268555
This analysis is done via One way ANOVA using Dehumanization of Natives as the DV
Now, we’ll revisit the relationship between dehumanizing whites and support for the two holidays, this time broken down by race.
First Columbus Day:
Then Indigenous Peoples’ Day:
Now, we’ll revisit the relationship between dehumanizing whites and support for the two holidays, this time broken down by race.
First Columbus Day:
Then Indigenous Peoples’ Day:
Now we’re going to take the opportunity to drill down a bit into the question of Native Stereotypes.
This time we’re going to pull the stereotype of Natives as Childlike as well as the stereotype of Natives as Uncivilized.
The way I’m going to do this is to pull both measures out of the dehumanization composite score and look at their effects on our DVs separately. To test this statistically we’ll place them into linear models together with the composite.
Let’s begin!
This first graph will show us regression lines for each of our 4 measures of dehumanization and their relationship with Support for abolishing Columbus Day
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## No_Columbus 2 2858 3.79 2.13 4.00 3.74 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH5_W_Childlike 5 2809 40.76 24.49 43.00 39.98 26.69 0
## DH5_N_Childlike 6 2802 29.22 22.26 25.00 27.68 28.17 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## No_Columbus 7.00 6.00 0.17 -1.31 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH5_W_Childlike 100.00 100.00 0.21 -0.55 0.46
## DH5_N_Childlike 100.00 100.00 0.54 -0.43 0.42
##
## Call:
## lm(formula = scale(No_Columbus, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH5_W_Childlike + DH5_N_Childlike, data = dvstereotypes.child.c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8633 -1.8083 -0.0807 1.7581 4.6669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.783234 0.188753 4.150 3.43e-05 ***
## White_Dehumanize -0.053827 0.004709 -11.431 < 2e-16 ***
## Native_Dehumanize 0.027957 0.004910 5.694 1.37e-08 ***
## DH5_W_Childlike 0.018252 0.001726 10.575 < 2e-16 ***
## DH5_N_Childlike -0.003985 0.001913 -2.082 0.0374 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.041 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.07825, Adjusted R-squared: 0.07693
## F-statistic: 59.03 on 4 and 2781 DF, p-value: < 2.2e-16
And we’ll do one for Indigenous Peoples’ Day as well:
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## Create_IPD 2 2858 4.48 1.96 4.00 4.60 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH5_W_Childlike 5 2809 40.76 24.49 43.00 39.98 26.69 0
## DH5_N_Childlike 6 2802 29.22 22.26 25.00 27.68 28.17 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## Create_IPD 7.00 6.00 -0.35 -0.97 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH5_W_Childlike 100.00 100.00 0.21 -0.55 0.46
## DH5_N_Childlike 100.00 100.00 0.54 -0.43 0.42
##
## Call:
## lm(formula = scale(Create_IPD, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH5_W_Childlike + DH5_N_Childlike, data = dvstereotypes.child.i)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5164 -1.3058 0.0292 1.6081 3.8440
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.033044 0.176129 -0.188 0.851
## White_Dehumanize -0.038655 0.004394 -8.798 < 2e-16 ***
## Native_Dehumanize 0.034383 0.004581 7.505 8.24e-14 ***
## DH5_W_Childlike 0.013216 0.001610 8.206 3.46e-16 ***
## DH5_N_Childlike -0.007674 0.001785 -4.298 1.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.905 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.05297, Adjusted R-squared: 0.05161
## F-statistic: 38.89 on 4 and 2781 DF, p-value: < 2.2e-16
Now we’ll take a look at stereotypes about being civilized using the same methods as before:
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## No_Columbus 2 2858 3.79 2.13 4.00 3.74 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH2_W_Civilized 5 2810 65.89 23.38 69.00 67.81 25.20 0
## DH2_N_Civilized 6 2806 64.58 21.97 65.00 65.70 22.24 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## No_Columbus 7.00 6.00 0.17 -1.31 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH2_W_Civilized 100.00 100.00 -0.69 0.20 0.44
## DH2_N_Civilized 100.00 100.00 -0.48 0.15 0.41
##
## Call:
## lm(formula = scale(No_Columbus, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH2_W_Civilized + DH2_N_Civilized, data = dvstereotypes.civil.c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9888 -1.7404 -0.0827 1.6746 4.8252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.949289 0.188085 5.047 4.77e-07 ***
## White_Dehumanize -0.008293 0.005669 -1.463 0.1436
## Native_Dehumanize 0.011076 0.005481 2.021 0.0434 *
## DH2_W_Civilized -0.028291 0.002188 -12.932 < 2e-16 ***
## DH2_N_Civilized 0.012068 0.002126 5.676 1.52e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.019 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.09807, Adjusted R-squared: 0.09678
## F-statistic: 75.6 on 4 and 2781 DF, p-value: < 2.2e-16
And we’ll do one for Indigenous Peoples’ Day as well:
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## Create_IPD 2 2858 4.48 1.96 4.00 4.60 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH2_W_Civilized 5 2810 65.89 23.38 69.00 67.81 25.20 0
## DH2_N_Civilized 6 2806 64.58 21.97 65.00 65.70 22.24 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## Create_IPD 7.00 6.00 -0.35 -0.97 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH2_W_Civilized 100.00 100.00 -0.69 0.20 0.44
## DH2_N_Civilized 100.00 100.00 -0.48 0.15 0.41
##
## Call:
## lm(formula = scale(Create_IPD, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH2_W_Civilized + DH2_N_Civilized, data = dvstereotypes.civil.i)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.530 -1.235 0.050 1.542 4.084
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.021525 0.176237 0.122 0.9028
## White_Dehumanize -0.009079 0.005312 -1.709 0.0875 .
## Native_Dehumanize 0.015670 0.005136 3.051 0.0023 **
## DH2_W_Civilized -0.018689 0.002050 -9.117 < 2e-16 ***
## DH2_N_Civilized 0.013673 0.001992 6.863 8.28e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.892 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.0656, Adjusted R-squared: 0.06425
## F-statistic: 48.81 on 4 and 2781 DF, p-value: < 2.2e-16
The story here seems to be that in the middle, when we’re talking about the average level of dehumanization, people are more or less treating the civilized question like they treat the other measures of dehumanization. However, when it comes to perceptions of Whites and Natives as childlike something else starts happening, particularly at the extremes. Whether there is a story there for These two measure in particular is difficult to say.
Let’s look at these measures broken down by race and then by education level:
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 5 2597224 519445 1329.337 < 2e-16 ***
## Race 3 12148 4049 10.363 8.26e-07 ***
## Group:Race 15 50081 3339 8.544 < 2e-16 ***
## Residuals 15317 5985192 391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 55 observations deleted due to missingness
And in case we haven’t done it, here’s the measure’s side by side without separating for race:
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 5 2597224 519445 1317 <2e-16 ***
## Residuals 15335 6047421 394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 55 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Rating ~ Group, data = dvstereotypes.race.lng)
##
## $Group
## diff lwr upr
## Native_Dehumanize-White_Dehumanize -1.938569 -3.524086 -0.3530521
## DH2_W_Civilized-White_Dehumanize 13.523152 11.939801 15.1065030
## DH2_N_Civilized-White_Dehumanize 11.390845 9.807186 12.9745039
## DH5_W_Childlike-White_Dehumanize -12.558589 -14.142093 -10.9750836
## DH5_N_Childlike-White_Dehumanize -23.887754 -25.471876 -22.3036316
## DH2_W_Civilized-Native_Dehumanize 15.461721 13.878526 17.0449164
## DH2_N_Civilized-Native_Dehumanize 13.329414 11.745911 14.9129174
## DH5_W_Childlike-Native_Dehumanize -10.620019 -12.203369 -9.0366701
## DH5_N_Childlike-Native_Dehumanize -21.949185 -23.533151 -20.3652181
## DH2_N_Civilized-DH2_W_Civilized -2.132307 -3.713642 -0.5509729
## DH5_W_Childlike-DH2_W_Civilized -26.081741 -27.662921 -24.5005606
## DH5_N_Childlike-DH2_W_Civilized -37.410906 -38.992704 -35.8291077
## DH5_W_Childlike-DH2_N_Civilized -23.949433 -25.530922 -22.3679447
## DH5_N_Childlike-DH2_N_Civilized -35.278599 -36.860705 -33.6964920
## DH5_N_Childlike-DH5_W_Childlike -11.329165 -12.911118 -9.7472129
## p adj
## Native_Dehumanize-White_Dehumanize 0.0065618
## DH2_W_Civilized-White_Dehumanize 0.0000000
## DH2_N_Civilized-White_Dehumanize 0.0000000
## DH5_W_Childlike-White_Dehumanize 0.0000000
## DH5_N_Childlike-White_Dehumanize 0.0000000
## DH2_W_Civilized-Native_Dehumanize 0.0000000
## DH2_N_Civilized-Native_Dehumanize 0.0000000
## DH5_W_Childlike-Native_Dehumanize 0.0000000
## DH5_N_Childlike-Native_Dehumanize 0.0000000
## DH2_N_Civilized-DH2_W_Civilized 0.0016998
## DH5_W_Childlike-DH2_W_Civilized 0.0000000
## DH5_N_Childlike-DH2_W_Civilized 0.0000000
## DH5_W_Childlike-DH2_N_Civilized 0.0000000
## DH5_N_Childlike-DH2_N_Civilized 0.0000000
## DH5_N_Childlike-DH5_W_Childlike 0.0000000
What we end up with is several possible stories. The big picture seems to be that in some cases it is the measure that is differentiating and other times the race of the participant seems to be driving differences, particularly in our civilized and childlike measures.
Now, let’s take a bit of a left turn and introduce another wrinkle into this analysis. How do these things break down by education level. For the sake of argument we’ll look just at college educated versus those who are not college educated.
## Df Sum Sq Mean Sq F value Pr(>F)
## Group 5 2754957 550991 1378.219 < 2e-16 ***
## College 1 2858 2858 7.148 0.00751 **
## Group:College 5 21292 4258 10.652 3.09e-10 ***
## Residuals 16713 6681609 400
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 693 observations deleted due to missingness
A simple interaction plot will show us these effects in a different form:
This will be where we put other questions and answers that we have.
EMP_comp:
| vars | n | mean | sd | median | |
|---|---|---|---|---|---|
| DH1_W_Responsible | 1 | 2811 | 61.24 | 21.82 | 62 |
| DH2_W_Civilized | 2 | 2810 | 65.89 | 23.38 | 69 |
| DH3_W_Moral | 3 | 2812 | 55.77 | 22.76 | 55 |
| DH4_W_Polite | 4 | 2812 | 57.99 | 22.53 | 58 |
| DH5_W_Childlike | 5 | 2809 | 40.76 | 24.49 | 43 |
| DH6_W_Rational | 6 | 2810 | 58.23 | 22.49 | 57 |
| DH7_W_Warm | 7 | 2810 | 58.27 | 22.32 | 58 |
| DH8_W_Agentic | 8 | 2800 | 46.11 | 22.34 | 50 |
| DH9_W_Refined | 9 | 2809 | 52.87 | 22.26 | 52 |
| DH10_W_Lacking_Culture | 10 | 2808 | 46.34 | 27.71 | 50 |
| DH11_W_Lacking_Self-restraint | 11 | 2808 | 48.59 | 25.58 | 50 |
| DH12_W_Instinctual | 12 | 2810 | 51.47 | 22.98 | 51 |
| DH13_W_Mature | 13 | 2809 | 57.34 | 22.31 | 56 |
| DH14_W_Stoic | 14 | 2808 | 42.88 | 22.31 | 49 |
| DH15_W_Emotionally_Responsive | 15 | 2812 | 59.98 | 22.77 | 60 |
| DH16_W_Cold | 16 | 2810 | 44.74 | 23.99 | 49 |
| DH17_W_Open | 17 | 2811 | 55.59 | 22.81 | 55 |
| DH18_W_Rigid | 18 | 2807 | 49.6 | 23.27 | 50 |
| DH19_W_Passive | 19 | 2810 | 45.16 | 23.21 | 49 |
| DH20_W_Superficial | 20 | 2810 | 59.32 | 25.54 | 60 |
| DH1_N_Responsible | 21 | 2807 | 63.86 | 21.06 | 64 |
| DH2_N_Civilized | 22 | 2806 | 64.58 | 21.97 | 65 |
| DH3_N_Moral | 23 | 2807 | 65.75 | 20.88 | 66 |
| DH4_N_Polite | 24 | 2806 | 63.22 | 21.42 | 63 |
| DH5_N_Childlike | 25 | 2802 | 29.22 | 22.26 | 25 |
| DH6_N_Rational | 26 | 2807 | 61.61 | 21.17 | 61 |
| DH7_N_Warm | 27 | 2804 | 61.05 | 22.07 | 60 |
| DH8_N_Agentic | 28 | 2800 | 44.97 | 22.32 | 50 |
| DH9_N_Refined | 29 | 2806 | 51.11 | 22.44 | 51 |
| DH10_N_Lacking_Culture | 30 | 2804 | 23.9 | 23.15 | 17 |
| DH11_N_Lacking_Self-restraint | 31 | 2805 | 34.44 | 23.39 | 33 |
| DH12_N_Instinctual | 32 | 2807 | 61.3 | 23.48 | 61 |
| DH13_N_Mature | 33 | 2806 | 65.15 | 20.69 | 65 |
| DH14_N_Stoic | 34 | 2808 | 59.11 | 22.29 | 57 |
| DH15_N_Emotionally_Responsive | 35 | 2806 | 54.38 | 24.29 | 53 |
| DH16_N_Cold | 36 | 2805 | 35.19 | 22.8 | 35 |
| DH17_N_Open | 37 | 2805 | 53.8 | 23.34 | 53 |
| DH18_N_Rigid | 38 | 2807 | 45.41 | 23.89 | 50 |
| DH19_N_Passive | 39 | 2805 | 45.03 | 23.79 | 50 |
| DH20_N_Superficial | 40 | 2805 | 30.41 | 22.87 | 27 |
| EMP* | 41 | 2903 | 1 | 0 | 1 |
| Mean Score | 42 | 2841 | 3.337 | 1.438 | 3.4 |
| trimmed | mad | min | max | range | |
|---|---|---|---|---|---|
| DH1_W_Responsible | 62.64 | 19.27 | 0 | 100 | 100 |
| DH2_W_Civilized | 67.81 | 25.2 | 0 | 100 | 100 |
| DH3_W_Moral | 56.82 | 22.24 | 0 | 100 | 100 |
| DH4_W_Polite | 59.29 | 20.76 | 0 | 100 | 100 |
| DH5_W_Childlike | 39.98 | 26.69 | 0 | 100 | 100 |
| DH6_W_Rational | 59.45 | 20.76 | 0 | 100 | 100 |
| DH7_W_Warm | 59.58 | 19.27 | 0 | 100 | 100 |
| DH8_W_Agentic | 46.85 | 11.86 | 0 | 100 | 100 |
| DH9_W_Refined | 53.56 | 20.76 | 0 | 100 | 100 |
| DH10_W_Lacking_Culture | 45.92 | 32.62 | 0 | 100 | 100 |
| DH11_W_Lacking_Self-restraint | 48.69 | 28.17 | 0 | 100 | 100 |
| DH12_W_Instinctual | 52.05 | 20.76 | 0 | 100 | 100 |
| DH13_W_Mature | 58.44 | 20.76 | 0 | 100 | 100 |
| DH14_W_Stoic | 42.9 | 20.76 | 0 | 100 | 100 |
| DH15_W_Emotionally_Responsive | 61.27 | 20.76 | 0 | 100 | 100 |
| DH16_W_Cold | 44.43 | 25.2 | 0 | 100 | 100 |
| DH17_W_Open | 56.57 | 22.24 | 0 | 100 | 100 |
| DH18_W_Rigid | 50.06 | 22.24 | 0 | 100 | 100 |
| DH19_W_Passive | 45.16 | 23.72 | 0 | 100 | 100 |
| DH20_W_Superficial | 60.99 | 25.2 | 0 | 100 | 100 |
| DH1_N_Responsible | 64.78 | 20.76 | 0 | 100 | 100 |
| DH2_N_Civilized | 65.7 | 22.24 | 0 | 100 | 100 |
| DH3_N_Moral | 66.67 | 22.24 | 0 | 100 | 100 |
| DH4_N_Polite | 64.15 | 19.27 | 0 | 100 | 100 |
| DH5_N_Childlike | 27.68 | 28.17 | 0 | 100 | 100 |
| DH6_N_Rational | 62.6 | 17.79 | 0 | 100 | 100 |
| DH7_N_Warm | 61.99 | 20.76 | 0 | 100 | 100 |
| DH8_N_Agentic | 45.62 | 11.86 | 0 | 100 | 100 |
| DH9_N_Refined | 51.46 | 19.27 | 0 | 100 | 100 |
| DH10_N_Lacking_Culture | 20.99 | 22.24 | 0 | 100 | 100 |
| DH11_N_Lacking_Self-restraint | 33.22 | 26.69 | 0 | 100 | 100 |
| DH12_N_Instinctual | 62.82 | 20.76 | 0 | 100 | 100 |
| DH13_N_Mature | 66.08 | 22.24 | 0 | 100 | 100 |
| DH14_N_Stoic | 60.12 | 20.76 | 0 | 100 | 100 |
| DH15_N_Emotionally_Responsive | 55.39 | 22.24 | 0 | 100 | 100 |
| DH16_N_Cold | 34.35 | 25.2 | 0 | 100 | 100 |
| DH17_N_Open | 54.26 | 22.24 | 0 | 100 | 100 |
| DH18_N_Rigid | 45.67 | 23.72 | 0 | 100 | 100 |
| DH19_N_Passive | 44.94 | 22.24 | 0 | 100 | 100 |
| DH20_N_Superficial | 28.75 | 29.65 | 0 | 100 | 100 |
| EMP* | 1 | 0 | 1 | 1 | 0 |
| Mean Score | 3.306 | 1.483 | 1 | 7 | 6 |
| skew | kurtosis | se | |
|---|---|---|---|
| DH1_W_Responsible | -0.5742 | 0.2696 | 0.4116 |
| DH2_W_Civilized | -0.6889 | 0.2007 | 0.4411 |
| DH3_W_Moral | -0.3719 | -0.1342 | 0.4292 |
| DH4_W_Polite | -0.4732 | 0.009527 | 0.4248 |
| DH5_W_Childlike | 0.2092 | -0.5542 | 0.462 |
| DH6_W_Rational | -0.4488 | 0.001843 | 0.4243 |
| DH7_W_Warm | -0.4832 | 0.07056 | 0.421 |
| DH8_W_Agentic | -0.3413 | 0.1309 | 0.4222 |
| DH9_W_Refined | -0.2509 | -0.1186 | 0.4199 |
| DH10_W_Lacking_Culture | 0.07817 | -0.8826 | 0.523 |
| DH11_W_Lacking_Self-restraint | -0.04029 | -0.7017 | 0.4828 |
| DH12_W_Instinctual | -0.2033 | -0.2257 | 0.4335 |
| DH13_W_Mature | -0.4135 | 0.04243 | 0.421 |
| DH14_W_Stoic | -0.04162 | -0.298 | 0.4209 |
| DH15_W_Emotionally_Responsive | -0.4721 | 0.002383 | 0.4294 |
| DH16_W_Cold | 0.09141 | -0.5112 | 0.4525 |
| DH17_W_Open | -0.3409 | -0.1675 | 0.4302 |
| DH18_W_Rigid | -0.1571 | -0.3567 | 0.4393 |
| DH19_W_Passive | 0.002147 | -0.3755 | 0.4378 |
| DH20_W_Superficial | -0.4857 | -0.3722 | 0.4818 |
| DH1_N_Responsible | -0.4596 | 0.317 | 0.3976 |
| DH2_N_Civilized | -0.482 | 0.1522 | 0.4147 |
| DH3_N_Moral | -0.527 | 0.4136 | 0.3941 |
| DH4_N_Polite | -0.4299 | 0.2116 | 0.4044 |
| DH5_N_Childlike | 0.5351 | -0.4322 | 0.4205 |
| DH6_N_Rational | -0.4534 | 0.336 | 0.3995 |
| DH7_N_Warm | -0.3667 | 0.02378 | 0.4167 |
| DH8_N_Agentic | -0.3395 | -0.007707 | 0.4219 |
| DH9_N_Refined | -0.1243 | -0.06897 | 0.4235 |
| DH10_N_Lacking_Culture | 0.978 | 0.2417 | 0.4371 |
| DH11_N_Lacking_Self-restraint | 0.3627 | -0.4915 | 0.4416 |
| DH12_N_Instinctual | -0.5158 | 0.04848 | 0.4433 |
| DH13_N_Mature | -0.5208 | 0.4368 | 0.3907 |
| DH14_N_Stoic | -0.3809 | 0.0361 | 0.4206 |
| DH15_N_Emotionally_Responsive | -0.3216 | -0.2435 | 0.4585 |
| DH16_N_Cold | 0.2317 | -0.6249 | 0.4306 |
| DH17_N_Open | -0.1702 | -0.3199 | 0.4406 |
| DH18_N_Rigid | -0.1095 | -0.5238 | 0.451 |
| DH19_N_Passive | 0.006644 | -0.4618 | 0.4492 |
| DH20_N_Superficial | 0.543 | -0.3894 | 0.4318 |
| EMP* | NA | NA | 0 |
| Mean Score | 0.1451 | -0.5044 | 0.02698 |